An R package for Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024), Lütkepohl & Woźniak (2020), and Song & Woźniak (2021).
A
and error terms E
, and the structural
equation with a structural matrix B
and shocks
U
Y = AX + E (VAR equation)
BE = U (structural equation)
A
and the structural
matrix B
feature a three-level local-global hierarchical
prior that estimates the equation-specific level of shrinkagespecify_bsvar_*
functions, for
instance, specify_bsvar_sv$new()
estimate()
methodforecast()
methodcompute_impulse_responses()
,
compute_variance_decompositions()
,
compute_historical_decompositions()
, and
compute_structural_shocks()
respectivelycompute_fitted_values()
,
compute_conditional_sd()
, and
compute_regime_probabilities()
respectivelyplot()
and summary()
methods to gain
the insights into the core of the empirical problem.verify_volatility()
and
verify_autoregression()
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bsvars: Bayesian Structural Vector Autoregressions|
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Gibbs sampler for the SVAR-SV model |
Non-centred SV model is estimated |
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Progress of the MCMC simulation for 1000 draws
Every 10th draw is saved via MCMC thinning
Press Esc to interrupt the computations
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This beautiful logo can be reproduced in R using this file.
The beginnings are as easy as ABC:
library(bsvars) # upload the package
data(us_fiscal_lsuw) # upload data
= specify_bsvar_sv$new(us_fiscal_lsuw, p = 4) # specify the model
spec = estimate(spec, 1000) # run the burn-in
burn_in = estimate(burn_in, 50000) # estimate the model
out
= forecast(out, horizon = 8) # forecast 2 years ahead
fore plot(fore) # plot the forecast
= compute_impulse_responses(out, 8) # compute impulse responses
irfs plot(irfs) # plot the impulse responses
The bsvars package supports a simplified workflow
using the |>
pipe:
library(bsvars) # upload the package
data(us_fiscal_lsuw) # upload data
|>
us_fiscal_lsuw $new(p = 4) |> # specify the model
specify_bsvar_svestimate(S = 1000) |> # run the burn-in
estimate(S = 50000) -> out # estimate the model
|> forecast(horizon = 8) |> plot() # compute and plot forecasts
out |> compute_impulse_responses(8) |> plot() # compute and plot impulse responses out
Now, you’re ready to analyse your model!
You must have a cpp compiler. Follow the instructions from Section 1.3. by Eddelbuettel & François (2023). In short, for Windows: install RTools, for macOS: install Xcode Command Line Tools, and for Linux: install the standard developement packages.
Just open your R and type:
install.packages("bsvars")
The developer’s version of the package with the newest features can be installed by typing:
devtools::install_github("bsvars/bsvars")
The package is under intensive development. Your help is most welcome! Please, have a look at the roadmap, discuss package features and applications, or report a bug. Thank you!
Tomasz is a Bayesian econometrician and a Senior Lecturer at the University of Melbourne. He develops methodology for empirical macroeconomic analyses and programs in R and cpp using Rcpp.